Collision-Aware Data Delivery Framework for Connected Vehicles via Edges

With the rapid advancements in communication technologies, the paradigm of connected vehicles is drastically transforming the automotive industry, enabling efficient data and service delivery to vehicles via edges. Various works have considered data delivery without a Medium Access Layer (MAC), which can result in multiple data frame collisions in the network. The time-slot-based MAC layer strategy uses slot assignment to ensure collision-free data delivery for multiple vehicles across various transmission channels at each edge. However, the increasing requests from various vehicular nodes can increase network congestion, thus servicing fewer vehicles. In the current work, we propose an optimization framework for collision-aware data delivery considering two state-of-art MAC protocols, HCCA and VeMAC. The proposed framework minimizes the global slot utilization cost for edge-to-vehicle data delivery, considering vehicle flow, edge resources, and vehicle overlaps while avoiding possible data transmission collisions. Further, we demonstrate the practicality of the framework in terms of the number of vehicles served, global slot utilization cost, and bandwidth cost. We further analyze the framework with differences in vehicle densities for various problem sizes using a real-world traffic scenario.